Search

Abstract Background We investigated the effects of a physical activity encouragement intervention based on a smartphone personal health record (PHR) application (app) on step count increases, glycemic control, and body weight in patients with type 2 diabetes (T2D). Methods In this 12-week, single-center, randomized controlled, 12-week extension study, patients with T2D who were overweight or obese were randomized using ratio 1:2 to a group using a smartphone PHR app (control group) or group using the app and received individualized motivational text messages (intervention group) for 12 weeks. During the extension period, the sending of the encouraging text messages to the intervention group was discontinued. The primary outcome was a change in daily step count after 12 weeks and analyzed by independent t-test. The secondary outcomes included HbA1c, fasting glucose, and body weight analyzed by paired or independent t-test. Results Of 200 participants, 62 (93.9%) and 118 (88.1%) in the control and intervention group, respectively, completed the 12-week main study. The change in daily step count from baseline to week 12 was not significantly different between the two groups (P = 0.365). Among participants with baseline step counts

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a well-known risk factor for cardiovascular (CV) disease (CVD) and mortality. However, whether the progression or regression of NAFLD can increase or decrease the risk of heart failure (HF) and mortality has not been fully evaluated. We investigated the association between changes in hepatic steatosis and the risks of incident HF (iHF), hospitalization for HF (hHF), and mortality including CV- or liver-related mortality. Methods Using a database from the National Health Insurance Service in Korea from January 2009 to December 2012, we analyzed 240,301 individuals who underwent health check-ups at least twice in two years. Hepatic steatosis was assessed using the fatty liver index (FLI), with an FLI ≥ 60 considered to indicate the presence of hepatic steatosis. According to FLI changes, participants were divided into four groups. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using multivariable Cox proportional hazards regression models. Results Persistent hepatic steatosis increased the risk of iHF, hHF, and mortality including CV- and liver-related mortality compared with the group that never had steatosis (all P

Abstract Background Although both type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD) are associated with increased risk of cardiovascular disease (CVD), evidence is lacking as to whether the presence of NAFLD confers an additional risk of CVD in patients with T2DM. We investigated the associations between hepatic steatosis and/or fibrosis and risk of myocardial infarction (MI), stroke, heart failure (HF), and mortality in patients with new-onset T2DM. Methods Using the Korean National Health Insurance dataset, we included 139,633 patients diagnosed with new-onset T2DM who underwent a national health screening from January 2009 to December 2012. Hepatic steatosis and advanced hepatic fibrosis were determined using cutoff values for fatty liver index (FLI) and BARD score. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using multivariable Cox proportional hazards regression models. Results During the median follow-up of 7.7 years, there were 3,079 (2.2%) cases of MI, 4,238 (3.0%) cases of ischemic stroke, 4,303 (3.1%) cases of HF, and 8,465 (6.1%) all-cause deaths. Hepatic steatosis defined as FLI ≥ 60 was associated with increased risk for MI (HR [95% CI], 1.28 [1.14–1.44]), stroke (1.41 [1.25–1.56]), HF (1.17 [1.07–1.26]), and mortality (1.41 [1.32–1.51]) after adjusting for well-known risk factors. Compared to the group without steatosis, the group with steatosis and without fibrosis (BARD

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic disease and independently affects the development of cardiovascular (CV) disease. We investigated whether hepatic steatosis and/or fibrosis are associated with the development of incident heart failure (iHF), hospitalized HF (hHF), mortality, and CV death in both the general population and HF patients. Methods We analyzed 778,739 individuals without HF and 7445 patients with pre-existing HF aged 40 to 80 years who underwent a national health check-up from January 2009 to December 2012. The presence of hepatic steatosis and advanced hepatic fibrosis was determined using cutoff values for fatty liver index (FLI) and BARD score. We evaluated the association of FLI or BARD score with the development of iHF, hHF, mortality and CV death using multivariable-adjusted Cox regression models. Results A total of 28,524 (3.7%) individuals in the general population and 1422 (19.1%) pre-existing HF patients developed iHF and hHF respectively. In the multivariable-adjusted model, participants with an FLI ≥ 60 were at increased risk for iHF (hazard ratio [HR], 95% confidence interval [CI], 1.30, 1.24–1.36), hHF (HR 1.54, 95% CI 1.44–1.66), all-cause mortality (HR 1.62, 95% CI 1.54–1.70), and CV mortality (HR 1.41 95% CI 1.22–1.63) in the general population and hHF (HR 1.26, 95% CI 1.21–1.54) and all-cause mortality (HR 1.54 95% CI 1.24–1.92) in the HF patient group compared with an FLI

Abstract Background Both type 1 and type 2 diabetes are well-established risk factors for cardiovascular disease and early mortality. However, few studies have directly compared the hazards of cardiovascular outcomes and premature death among people with type 1 diabetes to those among people with type 2 diabetes and subjects without diabetes. Furthermore, information about the hazard of cardiovascular disease and early mortality among Asians with type 1 diabetes is sparse, although the clinical and epidemiological characteristics of Asians with type 1 diabetes are unlike those of Europeans. We estimated the hazard of myocardial infarction (MI), hospitalization for heart failure (HF), atrial fibrillation (AF), and mortality during follow-up in Korean adults with type 1 diabetes compared with those without diabetes and those with type 2 diabetes. Methods We used Korean National Health Insurance Service datasets of preventive health check-ups from 2009 to 2016 in this retrospective longitudinal study. The hazard ratios of MI, HF, AF, and mortality during follow-up were analyzed using the Cox regression analyses according to the presence and type of diabetes in ≥ 20-year-old individuals without baseline cardiovascular disease (N = 20,423,051). The presence and type of diabetes was determined based on the presence of type 1 or type 2 diabetes at baseline. Results During more than 93,300,000 person-years of follow-up, there were 116,649 MIs, 135,532 AF cases, 125,997 hospitalizations for HF, and 344,516 deaths. The fully-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for incident MI, hospitalized HF, AF, and all-cause death within the mean follow-up of 4.6 years were higher in the type 1 diabetes group than the type 2 diabetes [HR (95% CI) 1.679 (1.490–1.893) for MI; 2.105 (1.901–2.330) for HF; 1.608 (1.411–1.833) for AF; 1.884 (1.762–2.013) for death] and non-diabetes groups [HR (95% CI) 2.411 (2.138–2.718) for MI; 3.024 (2.730–3.350) for HF; 1.748 (1.534–1.993) for AF; 2.874 (2.689–3.073) for death]. Conclusions In Korea, the presence of diabetes was associated with a higher hazard of cardiovascular disease and all-cause death. Specifically, people with type 1 diabetes had a higher hazard of cardiovascular disease and all-cause mortality compared to people with type 2 diabetes.

Abstract Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.

Abstract Background Skeletal muscle mass was negatively associated with metabolic syndrome prevalence in previous cross-sectional studies. The aim of this study was to investigate the impact of baseline skeletal muscle mass and changes in skeletal muscle mass over time on the development of metabolic syndrome in a large population-based 7-year cohort study. Methods A total of 14,830 and 11,639 individuals who underwent health examinations at the Health Promotion Center at Samsung Medical Center, Seoul, Korea were included in the analyses of baseline skeletal muscle mass and those changes from baseline over 1 year, respectively. Skeletal muscle mass was estimated by bioelectrical impedance analysis and was presented as a skeletal muscle mass index (SMI), a body weight-adjusted appendicular skeletal muscle mass value. Using Cox regression models, hazard ratio for developing metabolic syndrome associated with SMI values at baseline or changes of SMI over a year was analyzed. Results During 7 years of follow-up, 20.1% of subjects developed metabolic syndrome. Compared to the lowest sex-specific SMI tertile at baseline, the highest sex-specific SMI tertile showed a significant inverse association with metabolic syndrome risk (adjusted hazard ratio [AHR] = 0.61, 95% confidence interval [CI] 0.54–0.68). Furthermore, compared with SMI changes 1% changes in SMI over 1 year after additionally adjusting for baseline SMI and glycometabolic parameters. Conclusions An increase in relative skeletal muscle mass over time has a potential preventive effect on developing metabolic syndrome, independently of baseline skeletal muscle mass and glycometabolic parameters.

Abstract Background We investigated whether glycated albumin (GA) and its variability are associated with cardiovascular autonomic neuropathy (CAN) and further compared their associations with glycated hemoglobin (HbA1c). Methods This retrospective longitudinal study included 498 type 2 diabetic patients without CAN. CAN was defined as at least two abnormal results in parasympathetic tests or presence of orthostatic hypotension. The mean, standard deviation (SD), and coefficient of variance (CV) were calculated from consecutively measured GA (median 7 times) and HbA1c levels (median 8 times) over 2 years. Logistic regression analysis was used to compare the associations between CAN and GA- or HbA1c-related parameters. Receiver operating characteristic (ROC) curve analysis was used to compare the predictive power for CAN between GA- and HbA1c-related parameters. Results A total of 53 subjects (10.6%) developed CAN over 2 years. The mean, SD, and CV of GA or HbA1c were significantly higher in subjects with CAN. Higher mean GA and GA variability were associated with the risk of developing CAN, independent of conventional risk factors and HbA1c. In ROC curve analysis, the SD and CV of GA showed higher predictive value for CAN compared to the SD and CV of HbA1c, whereas the predictive value of mean GA did not differ from that of mean HbA1c. The mean, SD, and CV of GA showed additive predictive power to detect CAN development along with mean HbA1c. Conclusions Higher serum GA and its variability are significantly associated with the risk of developing CAN. Serum GA might be a useful indicator for diabetic complications and can enhance HbA1c’s modest clinical prediction for CAN.

Abstract Background A Personal Health Record (PHR) is an online application that allows patients to access, manage, and share their health data. PHRs not only enhance shared decision making with healthcare providers, but also enable remote monitoring and at-home-collection of detailed data. The benefits of PHRs can be maximized in insulin dose adjustment for patients starting or intensifying insulin regimens, as frequent self-monitoring of glucose, self-adjustment of insulin dose, and precise at-home data collection during the visit-to-visit period are important for glycemic control. The aim of this study is to examine the efficacy and safety of insulin dose adjustment based on a smartphone PHR application in patients with diabetes mellitus (DM) and to confirm the validity and stability of an information and communication technology (ICT)-based centralized clinical trial monitoring system. Methods This is a 24-week, open-label, randomized, multi-center trial. There are three follow-up measures: baseline, post-intervention at week 12, and at week 24. Subjects diagnosed with type 1 DM, type 2 DM, and/or post-transplant DM who initiate basal insulin or intensify their insulin regimen to a basal-bolus regimen are included. After education on insulin dose titration and prevention for hypoglycemia and a 1-week acclimation period, subjects are randomized in a 1:1 ratio to either an ICT-based intervention group or a conventional intervention group. Subjects in the conventional intervention group will save and send their health information to the server via a PHR application, whereas those in ICT-based intervention group will receive additional algorithm-based feedback messages. The health information includes level of blood glucose, insulin dose, details on hypoglycemia, food diary, and step count. The primary outcome will be the proportion of patients who reach an optimal insulin dose within 12 weeks of study enrollment, without severe hypoglycemia or unscheduled clinic visits. Discussion This clinical trial will reveal whether insulin dose adjustment based on a smartphone PHR application can facilitate the optimization of insulin doses in patients with DM. In addition, the process evaluation will provide information about the validity and stability of the ICT-based centralized clinical trial monitoring system in this research field. Trial registration Clinicaltrials.gov NCT 03112343 . Registered on 12 April 2017.

Abstract After publication of the original article [1] it was noted that both the figures and captions and relating to Figs. 1 and 2 had been interchanged.